Concurrent Particle Tracking using an iterative Kalman Filter approach

Particle tracking is a widespread research question for quantitative biology. In contrast to other approaches, we developed a local greedy technique based on the Kalman filter. To overcome the problem of guessing the first state of a particle, the algorithm runs iteratively in forward and backward direction. The algorithm was successfully tested with simulated and real data.